This is gshelley13’s first assignment for Geog458. Our course page can be accessed here.
On the weekends, when I’m not doing homework for Geog458, I go to this ski mountain
When thinking about mass-energy equivalence I use the equation \[E=mc^2\]
| Name | Age |
|---|---|
| Beck | 15 |
| Angie | 20 |
| Ray | 16 |
| DJ | 19 |
| Chris | 23 |
| Ami | 21 |
100/10+2
## [1] 12
100/(10+2)
## [1] 8.333333
25+3/4-6/100
## [1] 25.69
(25+3)/(4-6)/100
## [1] -0.14
1+250/40/(15-9)
## [1] 2.041667
x<-8*3
x+10
## [1] 34
(x/2)+(x*3)
## [1] 84
z<-9 + 6
y<-2 * 14
x + y
## [1] 52
(6*z) - (0.5*y)
## [1] 76
qt<-seq(1,30)
qt
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30
c(qt,qt,qt)
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## [24] 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## [47] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9
## [70] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
length(qt)
## [1] 30
length(c(qt,qt,qt))
## [1] 90
sum(qt)
## [1] 465
sum(c(qt,qt,qt))
## [1] 1395
bond<-seq(1,5)
james<-seq(6,10)
bond + james
## [1] 7 9 11 13 15
bond * james
## [1] 6 14 24 36 50
c(bond,james)
## [1] 1 2 3 4 5 6 7 8 9 10
jamie<-seq(1,5)
lee<-seq(6,10)
curtis<-jamie*lee
rbind(jamie,lee,curtis)
## [,1] [,2] [,3] [,4] [,5]
## jamie 1 2 3 4 5
## lee 6 7 8 9 10
## curtis 6 14 24 36 50
g<-rbind(jamie,lee,curtis)
data.frame(rbind(jamie,lee,curtis))
## X1 X2 X3 X4 X5
## jamie 1 2 3 4 5
## lee 6 7 8 9 10
## curtis 6 14 24 36 50
This is how to load data into R and how to convert it.
library(tidyverse)
## ── Attaching packages ─────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0 ✔ purrr 0.2.5
## ✔ tibble 2.0.1 ✔ dplyr 0.7.8
## ✔ tidyr 0.8.2 ✔ stringr 1.3.1
## ✔ readr 1.3.1 ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
object1=read.csv("/Users/gabbyshelley/Desktop/GIS directory/China_EO_49to171.csv",fileEncoding = "latin1")
object2=as_tibble(object1)
object2
## # A tibble: 69 x 63
## Year Beijing_Enterpr… Beijing_Output Tianjin_Enterpr… Tianjin_Output
## <int> <int> <dbl> <int> <dbl>
## 1 1949 21055 1.47 4708 7.29
## 2 1950 23461 3.07 6358 11.6
## 3 1951 29839 5.99 9190 17.3
## 4 1952 34386 7.19 82 18.5
## 5 1953 38632 10.3 8459 26.2
## 6 1954 39595 11.9 8918 29.4
## 7 1955 32036 13.2 7250 30.1
## 8 1956 3574 18.2 2710 35.7
## 9 1957 4234 19.9 1049 38.6
## 10 1958 2084 42.8 2376 61.9
## # … with 59 more rows, and 58 more variables: Hebei_Enterprise <int>,
## # Hebei_Output <dbl>, Shanxi_Enterprise <int>, Shanxi_Output <dbl>,
## # InnerMongolia_Enterprise <int>, InnerMongolia_Output <dbl>,
## # Liaoning_Enterprise <int>, Liaoning_Output <dbl>,
## # Jilin_Enterprise <int>, Jilin_Output <dbl>,
## # Heilongjiang_Enterprise <int>, Heilongjiang_Output <dbl>,
## # Shanghai_Enterprise <int>, Shanghai_Output <dbl>,
## # Jiangsu_Enterprise <int>, Jiangsu_Output <dbl>,
## # Zhejiang_Enterprise <int>, Zhejiang_Output <dbl>,
## # Anhui_Enterprise <int>, Anhui_Output <dbl>, Fujian_Enterprise <int>,
## # Fujian_Output <dbl>, Jiangxi_Enterprise <int>, Jiangxi_Output <dbl>,
## # Shandong_Enterprise <int>, Shandong_Output <dbl>,
## # Henan_Enterprise <int>, Henan_Output <dbl>, Hubei_Enterprise <int>,
## # Hubei_Output <dbl>, Hunan_Enterprises <int>, Hunan_Output <dbl>,
## # Guangdong_Enterprise <int>, Guangdong_Output <dbl>,
## # Guangxi_Enterprise <int>, Guangxi_Output <dbl>,
## # Hainan_Enterprise <int>, Hainan_Output <dbl>,
## # Chongqing_Enterprise <int>, Chongqing_Output <dbl>,
## # Sichuan_Enterprise <int>, Sichuan_Output <dbl>,
## # Guizhou_Enterprise <int>, Guizhou_Output <dbl>,
## # Yunnan_Enterprise <int>, Yunnan_Output <dbl>, Tibet_Enterprise <int>,
## # Tibet_Output <dbl>, Shaanxi_Enterprise <int>, Shaanxi_Output <dbl>,
## # Gansu_Enterprise <int>, Gansu_Output <dbl>, Qinghai_Enterprise <int>,
## # Qinghai_Output <dbl>, Ningxia_Enterprise <int>, Ningxia_Output <dbl>,
## # Xinjiang_Enterprise <int>, Xinjiang_Output <dbl>
arrange(object2,desc(Year))
## # A tibble: 69 x 63
## Year Beijing_Enterpr… Beijing_Output Tianjin_Enterpr… Tianjin_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2017 3231 NA 4286 NA
## 2 2016 3340 18087. 5203 27402.
## 3 2015 3548 17450. 5525 28017.
## 4 2014 3686 18453. 5501 28079.
## 5 2013 3641 17371. 5511 26400.
## 6 2012 3692 15596. 5342 23428.
## 7 2011 3746 14514. 5013 20863.
## 8 2010 6884 13700. 7947 16752.
## 9 2009 6890 11039. 8326 13084.
## 10 2008 7205 10413. 7950 12503.
## # … with 59 more rows, and 58 more variables: Hebei_Enterprise <int>,
## # Hebei_Output <dbl>, Shanxi_Enterprise <int>, Shanxi_Output <dbl>,
## # InnerMongolia_Enterprise <int>, InnerMongolia_Output <dbl>,
## # Liaoning_Enterprise <int>, Liaoning_Output <dbl>,
## # Jilin_Enterprise <int>, Jilin_Output <dbl>,
## # Heilongjiang_Enterprise <int>, Heilongjiang_Output <dbl>,
## # Shanghai_Enterprise <int>, Shanghai_Output <dbl>,
## # Jiangsu_Enterprise <int>, Jiangsu_Output <dbl>,
## # Zhejiang_Enterprise <int>, Zhejiang_Output <dbl>,
## # Anhui_Enterprise <int>, Anhui_Output <dbl>, Fujian_Enterprise <int>,
## # Fujian_Output <dbl>, Jiangxi_Enterprise <int>, Jiangxi_Output <dbl>,
## # Shandong_Enterprise <int>, Shandong_Output <dbl>,
## # Henan_Enterprise <int>, Henan_Output <dbl>, Hubei_Enterprise <int>,
## # Hubei_Output <dbl>, Hunan_Enterprises <int>, Hunan_Output <dbl>,
## # Guangdong_Enterprise <int>, Guangdong_Output <dbl>,
## # Guangxi_Enterprise <int>, Guangxi_Output <dbl>,
## # Hainan_Enterprise <int>, Hainan_Output <dbl>,
## # Chongqing_Enterprise <int>, Chongqing_Output <dbl>,
## # Sichuan_Enterprise <int>, Sichuan_Output <dbl>,
## # Guizhou_Enterprise <int>, Guizhou_Output <dbl>,
## # Yunnan_Enterprise <int>, Yunnan_Output <dbl>, Tibet_Enterprise <int>,
## # Tibet_Output <dbl>, Shaanxi_Enterprise <int>, Shaanxi_Output <dbl>,
## # Gansu_Enterprise <int>, Gansu_Output <dbl>, Qinghai_Enterprise <int>,
## # Qinghai_Output <dbl>, Ningxia_Enterprise <int>, Ningxia_Output <dbl>,
## # Xinjiang_Enterprise <int>, Xinjiang_Output <dbl>
select(object2, Year, Beijing_Enterprise, Beijing_Output, Shanghai_Enterprise, Shanghai_Output)
## # A tibble: 69 x 5
## Year Beijing_Enterpri… Beijing_Output Shanghai_Enterpr… Shanghai_Output
## <int> <int> <dbl> <int> <dbl>
## 1 1949 21055 1.47 20307 NA
## 2 1950 23461 3.07 20897 NA
## 3 1951 29839 5.99 24956 NA
## 4 1952 34386 7.19 25878 66.6
## 5 1953 38632 10.3 29873 91.5
## 6 1954 39595 11.9 28860 96.4
## 7 1955 32036 13.2 23713 91.4
## 8 1956 3574 18.2 18724 114.
## 9 1957 4234 19.9 16316 119.
## 10 1958 2084 42.8 14240 176.
## # … with 59 more rows
comp<-select(object2, Year, Beijing_Enterprise, Beijing_Output, Shanghai_Enterprise, Shanghai_Output)
filter(comp, Year == 2000 | Year == 2001 | Year == 2003 | Year == 2004 | Year == 2005 | Year == 2006 | Year == 2007 | Year == 2008 | Year == 2009 | Year == 2010 | Year == 2011 | Year == 2012 | Year == 2013 | Year == 2014 | Year == 2015 | Year == 2016 | Year == 2017)
## # A tibble: 17 x 5
## Year Beijing_Enterpri… Beijing_Output Shanghai_Enterpr… Shanghai_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2000 4572 2565. 8574 6205.
## 2 2001 4356 2909. 9762 7004.
## 3 2003 4019 3810. 11098 10343.
## 4 2004 6871 4881. 15766 12885.
## 5 2005 6300 6946. 14809 15768.
## 6 2006 6400 8210 14404 18573.
## 7 2007 6397 9648. 15099 22260.
## 8 2008 7205 10413. 18792 25121.
## 9 2009 6890 11039. 17906 24091.
## 10 2010 6884 13700. 16684 30114.
## 11 2011 3746 14514. 9962 32445.
## 12 2012 3692 15596. 9772 31548.
## 13 2013 3641 17371. 9796 32089.
## 14 2014 3686 18453. 9469 32665.
## 15 2015 3548 17450. 8994 31050.
## 16 2016 3340 18087. 8351 31136.
## 17 2017 3231 NA 8122 36094.
task14 <- filter(comp, Year == 2000 | Year == 2001 | Year == 2003 | Year == 2004 | Year == 2005 | Year == 2006 | Year == 2007 | Year == 2008 | Year == 2009 | Year == 2010 | Year == 2011 | Year == 2012 | Year == 2013 | Year == 2014 | Year == 2015 | Year == 2016 | Year == 2017)
task14
## # A tibble: 17 x 5
## Year Beijing_Enterpri… Beijing_Output Shanghai_Enterpr… Shanghai_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2000 4572 2565. 8574 6205.
## 2 2001 4356 2909. 9762 7004.
## 3 2003 4019 3810. 11098 10343.
## 4 2004 6871 4881. 15766 12885.
## 5 2005 6300 6946. 14809 15768.
## 6 2006 6400 8210 14404 18573.
## 7 2007 6397 9648. 15099 22260.
## 8 2008 7205 10413. 18792 25121.
## 9 2009 6890 11039. 17906 24091.
## 10 2010 6884 13700. 16684 30114.
## 11 2011 3746 14514. 9962 32445.
## 12 2012 3692 15596. 9772 31548.
## 13 2013 3641 17371. 9796 32089.
## 14 2014 3686 18453. 9469 32665.
## 15 2015 3548 17450. 8994 31050.
## 16 2016 3340 18087. 8351 31136.
## 17 2017 3231 NA 8122 36094.
mutate(task14,
Output_Ratio = Beijing_Output/Shanghai_Output)
## # A tibble: 17 x 6
## Year Beijing_Enterpr… Beijing_Output Shanghai_Enterp… Shanghai_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2000 4572 2565. 8574 6205.
## 2 2001 4356 2909. 9762 7004.
## 3 2003 4019 3810. 11098 10343.
## 4 2004 6871 4881. 15766 12885.
## 5 2005 6300 6946. 14809 15768.
## 6 2006 6400 8210 14404 18573.
## 7 2007 6397 9648. 15099 22260.
## 8 2008 7205 10413. 18792 25121.
## 9 2009 6890 11039. 17906 24091.
## 10 2010 6884 13700. 16684 30114.
## 11 2011 3746 14514. 9962 32445.
## 12 2012 3692 15596. 9772 31548.
## 13 2013 3641 17371. 9796 32089.
## 14 2014 3686 18453. 9469 32665.
## 15 2015 3548 17450. 8994 31050.
## 16 2016 3340 18087. 8351 31136.
## 17 2017 3231 NA 8122 36094.
## # … with 1 more variable: Output_Ratio <dbl>
final <- mutate(task14,
Output_Ratio = Beijing_Output/Shanghai_Output)
final
## # A tibble: 17 x 6
## Year Beijing_Enterpr… Beijing_Output Shanghai_Enterp… Shanghai_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2000 4572 2565. 8574 6205.
## 2 2001 4356 2909. 9762 7004.
## 3 2003 4019 3810. 11098 10343.
## 4 2004 6871 4881. 15766 12885.
## 5 2005 6300 6946. 14809 15768.
## 6 2006 6400 8210 14404 18573.
## 7 2007 6397 9648. 15099 22260.
## 8 2008 7205 10413. 18792 25121.
## 9 2009 6890 11039. 17906 24091.
## 10 2010 6884 13700. 16684 30114.
## 11 2011 3746 14514. 9962 32445.
## 12 2012 3692 15596. 9772 31548.
## 13 2013 3641 17371. 9796 32089.
## 14 2014 3686 18453. 9469 32665.
## 15 2015 3548 17450. 8994 31050.
## 16 2016 3340 18087. 8351 31136.
## 17 2017 3231 NA 8122 36094.
## # … with 1 more variable: Output_Ratio <dbl>
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